In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality.
Image enhancement, as used herein, refers only to processes which improve the output quality of the image, and not to internal operations, necessitated by particular processor limitations. Thus, for example, changing TRC for better reproduction of an image is an enhancement operation. However, reducing the number of colors representing image to place an image into a particular file format, such as GIF files, or .BMP files, is not an enhancement operation.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in black and white images. Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
Three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image. This is a common approach taken to reproduction. However, the output documents of such a system are sometimes not satisfactory to the ultimate customer.
In a second case of image enhancement, the image can always be processed. It turns out that an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of images, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further, the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved.
Many improvements can be made to an image, including luminance enhancement (e.g. U.S. Pat. No 5,450,502); sharpness enhancement (e.g., U.S. Pat. No. 5,363,209); exposure adjustment (e.g. U.S. Pat. No. 5,414,538); color balance correction (e.g., U.S. Pat. No. 5,357,352, U.S. Pat. No. 5,371,615) or contrast enhancement (U.S. Pat. No. 5,581,370); color saturation correction (e.g. U.S. Pat. No. 5,450,217), etc. These processes can be used together in a predictive mode that does not require iterative processing (e.g. U.S. Pat. No. 5,347,374). Generally, these processing methods operate by modifying a set of tonal reproduction curves (TRCs). The output image is achieved by using TRCs, operating either on the luminance channel of an image expressed LC.sub.1 C.sub.2 coordinates, or preferably on each channel in a color density space description of the image in Red-Green-Blue (RGB) coordinates. The entire contents of U.S. Pat. Nos. 5,450,502; 5,363,209; 5,414,538; 5,357,352; 5,371,615; 5,581,370; 5,450,217; and 5,347,374 are hereby incorporated by reference.
Moreover, automatic image enhancement must be selective in its application. For example, when processing a compound document, a document with independent regions such as graphics, text, halftones, photographs, etc., the image enhancement for one region may not necessarily be applicable to another region. Thus, the image enhancement routine must selectively apply one or more of the above noted correction processes to each independent region.
Conventionally, to determine the tonal correction for independent regions on compound documents, two separate scans of the document was required. More specifically, as illustrated in FIG. 2, the image would be initially scanned at step S1 and from the image data generated from this scan the desired image regions or windowing would be identified at step S2. Thus, upon the completion at step S2, the various windows or regions of the image being scanned will have been identified. Thereafter, at step S3, the image is scanned again and the image data generated therefrom is utilized in step S4 to generate histogram data for each identified region. In other words, conventionally, it took two scans of the image to generate the image regions and the histograms for each identified region.
After these two sets of data is generated, step S5 uses the information generated at step S2 and S4 to enhance the image data; i.e., create the tonal correction curve for the image data for that region so that the image data can be outputted at step S6. Thus, FIG. 2 illustrates a conventional automatic image enhancement routine for correcting tonal reproduction curves for independent regions on a compound image. Although the method described with respect to FIG. 2 produces a high quality image, this method and process negatively impacts the productivity of the reprographic system More specifically, by requiring two separate scans of the image to generate the window data and the histogram data, respectively, the automatic image enhancement routine impacts productivity by one-half. Therefore, it is desirable to achieve the automatic image enhancement improvement while eliminating any adverse impact upon productivity.